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Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Structure Learning and an Empirical Evaluation


Tempo di lettura: < 1 minuto

This paper develops a maximum pseudo-likelihood algorithm for learning the structure of the dynamic extension of Hybrid Random Field introduced in the companion paper [5].

The technique turns out to be a viable method for capturing the statistical (in)dependencies among the random variables within a sequence of patterns.

Complexity issues are tackled by means of adequate strategies from classic literature on probabilistic graphical models. A preliminary empirical evaluation is presented eventually.

Book Title: Lecture Notes in Computer Science

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